Saurabh Mahindre
Applied Researcher at eBay
New York, New York, India
Summary
👤
Senior🎓
Top SchoolSaurabh Mahindre is an applied researcher and AI engineer with 13 years of experience building and deploying ML systems across industry and academia, currently applying ML at Oracle and transitioning to an Applied Researcher role at eBay. He holds an MS in Machine Learning and has published at top venues (ICLR, EMNLP, ICCV, MICCAI) on efficient, robust, and fair AI, including work on few-shot multi-label learning and intersectional fairness for medical imaging. At Oracle he improved recommendation and ranking via representation learning and built production LLM evaluation and bias-mitigation pipelines for 100+ customer datasets; earlier roles include large-scale risk models at Paytm and compact efficient architectures at IBM Research. He contributes to open source (Google Summer of Code alumni, test automation and examples for the Shogun ML toolbox) and combines deep research rigor with practical production engineering across NLP, computer vision, and fairness. Notably, his research blends geometric ensemble methods and novel contrastive/fairness loss functions to deliver measurable improvements on resource-constrained and imbalanced multi-label tasks.
13 years of coding experience
9 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceMaster of Science - MS Engineering Sciences: Data Science, Master of Science - MS Engineering Sciences: Data Science at University at Buffalo
English, Hindi, Marathi
Github Skills (11)
unit-testing10
c-language10
cprogramming-language10
data-structure9
metric9
evaluation9
machine-learning9
algorithm9
data-structures9
algorithms9
cmake7
Programming languages (8)
Github contributions (5)
shogun-toolbox/shogun
Dec 2013 - Dec 2017
Shōgun
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:100 commits, 42 PRs, 232 comments in 4 years
Contributions summary:Saurabh's contributions primarily involve removing files and modifying test files for the `shogun` repository, which is a machine learning toolbox. The code differences show changes in unit tests related to cross-validation, indicating the user's focus on ensuring the accuracy and functionality of the library's evaluation components. They added tests for edge cases and index covering, demonstrating a commitment to thorough testing of the library's machine learning capabilities. Further contributions included the creation of examples.
cmakedata-sciencegunc-plus-plusmachine-learning
Saurabh7/Saurabh7.github.io
Aug 2016 - Mar 2025
Contributions:20 pushes, 1 branch in 8 years 8 months
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